Technical Field
Embodiments of the present disclosure are directed to methods and systems for fault diagnosis for machine condition monitoring.
Discussion of the Related Art
Data-driven methods have received increasing attention in fault diagnosis of machine condition monitoring in recent years. In contrast to rule-based expert systems, data-driven approaches do not need extensive knowledge of a machine, making it easy to apply the same principles to different applications with little adaptation. In addition, data-driven algorithms that can adopt state-of-the-art techniques in pattern recognition or supervised machine learning tend to have greater generalization capability with respect to future test samples.
However, one challenge with regard to data driven approaches is the poor availability of training samples, specifically the failure training samples. In the life span of a typical machine, such as a gas turbine or an airplane, the machine should, most of the time, be in a normal, healthy, state. Only in a rare case can it experience some type of failure. Therefore, obtaining normal training data is not an issue, but acquiring failure samples is challenging. Even if at least one failure sample per failure type can be obtained, these samples are very likely to come from different machines.
FIG. 1 illustrates this need for properly transferring failure samples. Machine 1, the source, has both normal training samples 11 and failure training samples 12 in FIG. 1(a). Machine 2, a target, only has normal training data 13 in FIG. 1(b). How can failure training data from machine 1 be used to help train a pattern recognition algorithm for machine 2? Copying the failure data as is by overlaying the data, as shown in FIG. 1(c), will not work because machine 1 and machine 2 may have quite different operating behaviors and a failure on machine 2 may look different from the same failure occurring on machine 1. As a consequence, the copied failure samples mistakenly overlap with the normal training samples, as shown in FIG. 1(c).
Transfer learning is an active research field in machine learning and may be used to address these sample transferring challenges. There are generally two approaches applicable to this situation.                A. Re-weighting failure samples: This approach assigns a larger weight to training samples in the target domain (machine 2) and smaller weights to training samples in the source domain (machine 1). Alternatively it assigns a larger weight to source domain training samples that are closer to the data distribution of the target domain. However, it requires an assumption: the behavior of machine 1 must be similar to behavior of machine 2. This assumption is often not true in machine condition monitoring, as shown for example, in FIG. 1(c).        B. Applying a transformation to failure samples: The failure samples in the source domain are mapped to the target domain through a linear or a nonlinear transformation. One challenge is how to constrain this mapping because there are so many options. Correspondence is one of such constraints. For example, it may be assumed that certain samples from the source domain should be closer to certain samples from the target domain after the transformation. This type of correspondence is usually not available between machines.        